fix(context): accept analyzer vocabulary in causal edges (#1184)

get_causal_chain() matched only the canonical uppercase spellings
(CAUSED, INFLUENCED, PRECEDENT_FOR), while CausalChainAnalyzer's
vocabulary includes the present-tense forms (causes, influences,
leads_to, supports) — and the two differ in word form, not just case,
so case-insensitive matching alone would still miss them. An edge
recorded as "causes" produced an empty audit chain.

Storage normalizes both vocabularies onto the canonical types via
_CAUSAL_EDGE_ALIASES; traversal accepts the union (_CAUSAL_TRAVERSAL_TYPES).
add_causal_relationship() now accepts either spelling and stores the
canonical form.

semantica/context/context_graph.py

@@ -438,6 +438,23 @@ _ATTRS_MISSING = object()
 #: entities and timestamps.
 _CAUSAL_EDGE_TYPES = ("CAUSED", "INFLUENCED", "PRECEDENT_FOR")
 
+# Causal edges circulate under two vocabularies: this module's canonical
+# spellings above, and the present-tense spellings CausalChainAnalyzer also
+# accepts ("causes", "influences", "leads_to", "supports"). The present-tense
+# forms normalize onto the canonical types for storage; traversal accepts
+# both vocabularies so an edge recorded either way is never invisible.
+_CAUSAL_EDGE_ALIASES = {
+    "CAUSES": "CAUSED",
+    "CAUSED": "CAUSED",
+    "INFLUENCES": "INFLUENCED",
+    "INFLUENCED": "INFLUENCED",
+    "PRECEDES": "PRECEDENT_FOR",
+    "PRECEDENT_FOR": "PRECEDENT_FOR",
+}
+_CAUSAL_TRAVERSAL_TYPES = frozenset(_CAUSAL_EDGE_ALIASES) | {
+    "LEADS_TO", "LEAD_TO", "SUPPORTS", "SUPPORT",
+}
+
 
 class ContextGraph:
     """
@@ -2745,9 +2762,12 @@ class ContextGraph:
             target_decision_id: Target decision ID
             relationship_type: Type of relationship (CAUSED, INFLUENCED, PRECEDENT_FOR)
         """
-        valid_types = ["CAUSED", "INFLUENCED", "PRECEDENT_FOR"]
-        if relationship_type not in valid_types:
-            raise ValueError(f"Relationship type must be one of: {valid_types}")
+        # Normalize so callers may use either vocabulary's spelling
+        # ("causes" from CausalChainAnalyzer, or "CAUSED" from this module's
+        # canonical constant); the stored form is always canonical.
+        relationship_type = _CAUSAL_EDGE_ALIASES.get(relationship_type.upper())
+        if relationship_type is None:
+            raise ValueError(f"Relationship type must be one of: {_CAUSAL_EDGE_TYPES}")
         
         # Check if decisions exist - if not, skip adding relationship
         if source_decision_id not in self.nodes or target_decision_id not in self.nodes:
@@ -2839,11 +2859,11 @@ class ContextGraph:
             # Find connected decisions
             for edge in self.edges:
                 if direction == "upstream":
-                    if edge.target_id == current_id and edge.edge_type in ["CAUSED", "INFLUENCED", "PRECEDENT_FOR"]:
+                    if edge.target_id == current_id and edge.edge_type.upper() in _CAUSAL_TRAVERSAL_TYPES:
                         if edge.source_id not in visited and depth < max_depth:
                             queue.append((edge.source_id, depth + 1))
                 else:  # downstream
-                    if edge.source_id == current_id and edge.edge_type in ["CAUSED", "INFLUENCED", "PRECEDENT_FOR"]:
+                    if edge.source_id == current_id and edge.edge_type.upper() in _CAUSAL_TRAVERSAL_TYPES:
                         if edge.target_id not in visited and depth < max_depth:
                             queue.append((edge.target_id, depth + 1))
         

tests/context/test_decision_causal_edge_regression.py

@@ -323,3 +323,51 @@ def test_entity_based_inference_still_applies_without_explicit_edges():
         hop["from"] == earlier and hop["to"] == later and hop["type"] == "influences"
         for hop in hops
     )
+
+
+def test_get_causal_chain_accepts_lowercase_causal_edge_types():
+    """Issue #1184: edges recorded with the analyzer's lowercase vocabulary
+    must be traversed by get_causal_chain().
+
+    CausalChainAnalyzer documents causal types as lowercase ("causes",
+    "influences", ...) while get_causal_chain() matched only the uppercase
+    spellings, so an edge recorded as "causes" produced an empty audit
+    chain — silent and in the dangerous direction.
+    """
+    graph = ContextGraph(advanced_analytics=True)
+    cause = graph.record_decision(
+        category="a", scenario="upstream", reasoning="r",
+        outcome="x", confidence=0.9,
+    )
+    effect = graph.record_decision(
+        category="b", scenario="downstream", reasoning="r",
+        outcome="y", confidence=0.9,
+    )
+    graph.add_edge(cause, effect, "causes")
+
+    chain = graph.get_causal_chain(effect, direction="upstream")
+
+    assert [decision.decision_id for decision in chain] == [cause]
+
+
+def test_add_causal_relationship_accepts_any_case_and_stores_canonical():
+    """Issue #1184: add_causal_relationship() should accept either spelling
+    and store the canonical uppercase vocabulary."""
+    graph = ContextGraph(advanced_analytics=True)
+    cause = graph.record_decision(
+        category="a", scenario="upstream", reasoning="r",
+        outcome="x", confidence=0.9,
+    )
+    effect = graph.record_decision(
+        category="b", scenario="downstream", reasoning="r",
+        outcome="y", confidence=0.9,
+    )
+
+    graph.add_causal_relationship(cause, effect, relationship_type="causes")
+
+    edges = [
+        edge for edge in graph.edges
+        if edge.source_id == cause and edge.target_id == effect
+    ]
+    assert edges, "add_causal_relationship must store the edge"
+    assert edges[0].edge_type == "CAUSED"